Claude Opus vs Sonnet vs Haiku: Which Model for Your Coding Agent

Quick answer

Opus delivers the strongest reasoning for complex refactoring and multi-file changes but costs ~15× more than Haiku and runs slower. Sonnet balances speed, cost, and capability for most everyday coding tasks. Haiku excels at rapid iteration, approval loops, and cost-sensitive workflows where you need fast feedback on straightforward changes.

Key takeaways

• Opus is the model family to consider for deep reasoning, large refactors, and ambiguous requirements. Use it when correctness and architectural insight matter more than speed or cost.
• Sonnet is usually the balanced default for real-world coding: fast enough for interactive feedback and capable enough for most tasks.
• Haiku is the fast, low-cost option for quick fixes, test generation, and well-scoped follow-up work.
• Claude’s native store can make CLI and IDE resume comparatively cheap, but model changes should be described as choices made in the agent UI or conversation settings, not as a guarantee that every surface exposes the same controls.
• The right model depends on task complexity, your approval tolerance, and whether you’re running one deep conversation or several parallel workstreams.
• Benchmarking your own codebase reveals which model wins for your specific patterns - a Haiku conversation that needs three approval rounds may cost less and finish faster than one Opus attempt.

Speed, Cost, and Quality: The Core Trade-offs

When you direct a Claude coding agent from your phone, you care about three dimensions: how fast the model responds, how much each conversation costs, and how often it gets the task right on the first try. Opus vs Sonnet vs Haiku represents a classic engineering trade-off curve.

Opus is the model family you reach for when the task needs the deepest reasoning available in your account: ambiguous instructions, large refactors, subtle bugs, or architecture decisions. It is usually slower and more expensive than the lighter Claude options.

Sonnet is the practical default for many coding sessions. It is responsive enough for interactive work and strong enough for feature implementation, bug fixes, code review, and everyday refactoring.

Haiku is optimized for speed and cost. It fits well-scoped work such as quick fixes, simple tests, documentation, and follow-up edits after the hard reasoning is already done.

The practical question is not “which model is best” but “which model is best for this task, right now.” Start with the model your agent UI recommends for the work, then switch from the menu or settings when the task gets harder or simpler.

Opus for Complex Reasoning and Multi-Step Refactoring

Opus is the model you reach for when the task requires deep understanding, multi-file coordination, or reasoning about edge cases the agent hasn’t seen explicitly. It’s the strongest at:

• Ambiguous or underspecified requirements: when you describe a feature in high-level terms and expect the agent to infer the right architecture, Opus is more likely to get it right on the first try.
• Large refactoring across many files: renaming a core abstraction, splitting a module, or untangling circular dependencies - tasks where the agent must hold a mental model of distant code and reason about side effects.
• Debugging subtle issues: when a bug involves timing, concurrency, or interactions between layers, Opus can trace through the logic more reliably than Haiku or Sonnet.
• Architectural decisions: choosing between design patterns, evaluating trade-offs, or proposing a structure for a new feature - Opus provides more thoughtful, context-aware suggestions.

The cost is real. If you run a stronger model on a large codebase with frequent back-and-forth, one conversation can consume a lot of tokens. The speed penalty also matters: Opus responses take longer to generate, which means more time waiting for approval prompts or next steps when you’re checking in from your phone.

Use Opus when correctness and insight justify the cost and delay. If you’re refactoring a critical module, migrating a database schema, or implementing a feature with unclear requirements, Opus reduces the risk of expensive mistakes. If you’re fixing a typo or generating boilerplate, Haiku or Sonnet will finish faster and cheaper.

Sonnet for Balanced Coding Tasks and Real-Time Feedback

Sonnet is the workhorse model for most developers running coding agents. It’s fast enough to feel interactive, capable enough to handle the majority of coding tasks, and cheap enough to use all day without worrying about cost.

Claude 3.5 Sonnet and the newer 3.7 Sonnet both deliver strong performance on:

• Feature implementation: translating a spec or user story into working code, including tests and documentation.
• Bug fixes: diagnosing an issue from a stack trace or error message, proposing a fix, and applying it.
• Test generation: writing unit tests, integration tests, or property-based tests for existing code.
• Code review and cleanup: refactoring a function, improving naming, or applying linting rules.
• Iterative development: building a feature in stages, where each step depends on feedback from the previous one.

Sonnet’s speed advantage matters when you’re driving the agent from a phone. If the agent asks for approval or hits a quota, you want a response in seconds, not minutes. Sonnet delivers that responsiveness. It’s also cheap enough to run multiple parallel conversations across different folders or workstreams without worrying about cost blowout.

A practical workflow is to start most everyday coding conversations in Sonnet. If the agent struggles, produces incorrect code, or asks clarifying questions that suggest it’s missing context, escalate to Opus. If the task is simpler than expected - generating a config file, writing a README, or applying a mechanical refactor - you can drop to Haiku for the next iteration. Sonnet is the safe default.

Haiku for Rapid Iteration and Cost-Sensitive Workflows

Haiku is the fastest, cheapest model in the Claude family, and it’s more capable than many developers expect. It’s not a “dumb” model - it’s optimized for speed and cost, which makes it ideal for workflows where you iterate rapidly or run many parallel conversations.

When Haiku wins:

• Approval loops: when an agent asks “should I proceed with this change?” or “which file should I edit next?”, Haiku can evaluate the context and respond in seconds. You’re not paying for deep reasoning; you’re paying for a quick decision.
• Quick fixes and one-liners: fixing a typo, updating a dependency version, or adding a missing import - tasks where the solution is obvious and you just need the agent to apply it.
• Test generation for simple functions: writing unit tests for pure functions or straightforward logic - Haiku handles this well and finishes fast.
• Boilerplate and scaffolding: generating a new module, a config file, or a CRUD endpoint from a template - Haiku is fast and accurate for mechanical tasks.
• Cost-sensitive parallel workflows: if you’re running five conversations at once across different folders, Haiku’s low cost per token means you can keep all five moving without worrying about the bill.

Haiku’s weakness is complex reasoning. If the task requires understanding distant code, inferring unstated requirements, or reasoning about edge cases, Haiku is more likely to produce incorrect code or ask for clarification. The trade-off: you can run three Haiku iterations for the cost of one Sonnet attempt, so even if Haiku needs a retry, it may still finish faster and cheaper overall.

The key insight: Haiku is not a fallback for when you’re too cheap to use Sonnet. It’s a deliberate choice for workflows where speed and cost matter more than maximum reasoning depth. If you’re triaging agent notifications from your phone during the day, Haiku lets you unblock conversations instantly without burning through your budget.

How Agent Surface and Conversation History Affect Model Choice

Claude’s architecture matters here. The native Claude store can be shared across CLI and IDE surfaces, so opening or resuming across those surfaces is comparatively cheap. Model choice is still a user-visible setting, and the exact control depends on the surface you are using.

Practical implications:

• If you start in a lighter model and realize the task is harder than expected, switch to Sonnet or Opus from the model menu or settings where the surface supports it.
• If you start a Claude conversation in an IDE/webview and want to continue from your phone, MobileVibe can open or resume the matching local conversation and preserve the relevant context exposed by the native store.
• If the hard reasoning is done and the next step is mechanical, switch to a lighter model from the UI before sending the next message where that control is available.

This flexibility is unique to Claude. Codex (OpenAI’s coding agent) has separate CLI and IDE surfaces, and moving history between them can fork a copy. Cursor and Windsurf are standalone host apps, not VS Code, so their conversation stores are independent. Claude’s shared store across models and surfaces makes it easier to escalate or de-escalate mid-conversation.

When model choice matters most: if you’re running a conversation that spans multiple sessions (start on desktop, resume from phone, finish on desktop), the model you choose affects how much context the agent can hold. Opus can handle longer conversations with more context, but it’s slower and more expensive. Haiku is faster and cheaper, but it may lose track of distant context in a very long conversation. Sonnet balances context window and cost for most multi-session workflows.

Switching Models Mid-Conversation: When and How

Claude makes model changes a normal part of the workflow in supported surfaces. Use the model menu or conversation settings to escalate or de-escalate before the next message.

When to escalate:

• The agent produces incorrect code or asks clarifying questions that suggest it’s missing context.
• The task turns out to be more complex than you expected - a “quick fix” becomes a multi-file refactor.
• You hit a reasoning bottleneck: the agent can’t figure out the right approach, even after you provide more context.

When to de-escalate:

• The hard reasoning is done, and the next step is mechanical - applying a change, generating tests, or updating documentation.
• You’re in an approval loop and just need a quick decision, not deep reasoning.
• You’re running multiple parallel conversations and want to keep costs down.

How to switch: use the model menu or settings in the Claude surface you are using. If you are resuming from MobileVibe, treat MobileVibe as the control surface that opens or continues the local conversation; change the model through the available UI control before sending the next message.

The practical workflow: start in Sonnet for most tasks. If the agent struggles, escalate to Opus. If the task is simpler than expected, drop to Haiku for the next iteration. If you’re checking in from your phone and just need to approve a change or answer a quick question, Haiku is fast enough to keep the conversation moving.

Running Multiple Models in Parallel Across Workstreams

One of the most powerful patterns for developers who drive coding agents from their phone: run multiple conversations in parallel, each in a different folder or git worktree, each with a different model chosen for the task at hand.

Example workflow:

• Workstream 1 (main branch, complex feature): Opus conversation in the main project folder, working on a multi-file refactor that requires deep reasoning.
• Workstream 2 (feature branch, quick fix): Sonnet conversation in a git worktree, fixing a bug and writing tests.
• Workstream 3 (docs branch, boilerplate): Haiku conversation in another worktree, generating API documentation from code comments.

Each conversation runs independently. Each can use a different model. Each can be in a different state: one waiting for approval, one still working, one blocked on a quota. When you check your phone, MobileVibe’s dashboard shows all three conversations and what needs attention. You can approve the Haiku conversation in seconds, check the Sonnet conversation’s progress, and escalate the Opus conversation if it’s stuck.

This parallel workflow is where model choice compounds. If you run every conversation on the strongest model, you spend more than necessary on routine work. If you run all three in Haiku, the Opus workstream will struggle and need multiple retries. Matching the model to the task - Opus for complex reasoning, Sonnet for balanced work, Haiku for rapid iteration - lets you move faster and spend less overall.

MobileVibe’s role: the conversation-first dashboard makes it easy to manage multiple models across multiple folders. Each conversation shows its model, its folder, and its state. You can resume any conversation in its last surface (CLI or IDE) or continue it from your phone. You can set auto-approve rules per conversation, so Haiku conversations can proceed without manual approval while Opus conversations require confirmation. The result: you can run a mixed set of conversations and manage their state from a single mobile-friendly interface.

Benchmarking Your Own Codebase: Which Model Wins for You

The only way to know which model works best for your codebase is to benchmark real tasks. The trade-offs between Opus, Sonnet, and Haiku depend on your code’s complexity, your tolerance for approval loops, and your cost sensitivity.

How to benchmark:

1. Pick a representative task: choose a task you do often - implementing a feature, fixing a bug, writing tests, refactoring a module. Make sure it’s neither trivial nor impossibly hard.
2. Run the task in all three models: start three separate conversations (in different folders or git worktrees) and give each model the same instructions. Let each conversation run to completion.
3. Measure time, cost, and correctness: track how long each conversation takes (wall-clock time from start to finish), how much it costs (total tokens × model price), and whether the result is correct (does it pass tests, does it meet the spec, does it need manual fixes).
4. Count approval loops: if the agent asks for approval or clarification, count how many times you had to intervene. A Haiku conversation that needs three approval rounds may still finish faster than one Opus attempt that gets it right on the first try.
5. Repeat with different task types: benchmark a complex refactor, a quick fix, a test generation run, and a feature implementation. The winner will vary by task type.

What you’ll learn:

• For your codebase, Haiku may be faster and cheaper than Sonnet for 60% of tasks, even if it needs an extra approval round.
• For complex refactoring, Opus may save time overall by getting it right on the first try, even though it’s slower and more expensive per token.
• For approval loops, Haiku’s speed advantage compounds: if you’re checking in from your phone every 10 minutes, Haiku’s fast responses keep the conversation moving, while Opus’s slow responses create dead time.

The key insight: the “best” model is not universal. It depends on your codebase, your workflow, and your priorities. Benchmarking reveals the real trade-offs for your specific context.

FAQ

How much cheaper is Haiku than Opus?

Haiku is designed to be the lower-cost, faster Claude option, while Opus-class models are intended for heavier reasoning. Exact pricing and ratios change by provider, date, and input/output mix, so check your current Anthropic plan or provider billing page before using hard numbers in a budget.

Is Haiku weaker than Sonnet for coding tasks?

Haiku is optimized for speed and cost, not maximum reasoning depth. For straightforward tasks - quick fixes, boilerplate generation, test writing, approval loops - Haiku performs nearly as well as Sonnet and finishes much faster. For complex tasks that require multi-file reasoning, ambiguous requirements, or deep debugging, Haiku is more likely to produce incorrect code or ask for clarification. The trade-off: you can run multiple Haiku iterations for the cost of one Sonnet attempt, so even if Haiku needs a retry, it may still finish faster and cheaper overall.

When should I use Opus instead of Sonnet?

Use Opus when correctness and insight justify the extra cost and time. Specific scenarios: large refactoring across many files, debugging subtle issues that require tracing through distant code, implementing features with ambiguous or underspecified requirements, and making architectural decisions where the agent must evaluate trade-offs. If the task is well-defined and the solution is straightforward, Sonnet is faster and cheaper. If the task is complex and a mistake would be expensive to fix, Opus reduces risk.

Can I switch models mid-conversation without losing context?

Yes. Claude’s native conversation store syncs history across all three models (Opus, Sonnet, Haiku) and across both surfaces (CLI and IDE). You can start a conversation in Haiku, escalate to Sonnet or Opus mid-thread, and the agent sees the full history. This makes it easy to start with a fast, cheap model and escalate only when necessary. Codex, Cursor, and Windsurf do not share conversation history across models or surfaces in the same way.

Does Claude’s native store sync conversation history across Opus, Sonnet, and Haiku?

Yes. The native store is shared across all three models and both surfaces (CLI and IDE). When you switch models mid-conversation, the agent sees the full history, including messages sent to the previous model. This is a key advantage of Claude’s architecture: you can escalate or de-escalate models without forking the conversation or losing context. In supported surfaces, you can change models while keeping the conversation context available.

Is it worth running Haiku for approval loops and Opus for complex refactoring?

Yes, this is a strong pattern for cost-sensitive workflows. Use Haiku for approval loops, quick decisions, and mechanical tasks where speed matters more than deep reasoning. Use Opus for complex refactoring, architectural decisions, and tasks where a mistake would be expensive to fix. Use Sonnet for everything in between. Running a mixed fleet of models - each optimized for its task - lets you move faster and spend less overall. MobileVibe’s conversation-first dashboard makes it easy to manage multiple models across multiple folders, so you can run Haiku, Sonnet, and Opus in parallel and switch between them from your phone.

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If you’re running Claude coding agents and want to manage Opus, Sonnet, and Haiku conversations from your phone - checking approval loops, switching models mid-conversation, and running multiple workstreams in parallel - try MobileVibe free. It connects to your own desktop (macOS or Windows), keeps your agent sessions running locally, and gives you a mobile-friendly dashboard to resume any conversation in seconds. No credit card, no cloud sandbox - just your real agents, reachable from anywhere.